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Original Article
AI-Enhanced Educational Assistant with Real-Time Q&A and Dynamic Support + Extra Features
Afrin Nazir1
Dr. Mohd Rafi Ahmed2
1 Student, MCA, Deccan College of Engineering and Technology, Hyderabed, Telangana, India. 2 Associate Professor, MCA, Deccan College of Engineering and Technology, Hyderabed, Telangana, India.
Published Online: September-October 2025
Pages: 01-06
Cite this article
↗ https://www.doi.org/10.59256/ijsreat.20250505001
References
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17. M. Alqahtani and A. Kavakli, “AI in e-learning: A review of the role of chatbots,” Applied Sciences, vol. 10, no. 15, pp. 5115–5128, 2020.
18. D. Litman and S. Singh, “Intelligent tutoring systems: An overview,” AI Magazine, vol. 40, no. 4, pp. 8–15, 2019.
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20. C. Popenici and S. Kerr, “Exploring the impact of artificial intelligence on teaching and learning in higher education,” Research and Practice in Technology Enhanced Learning, vol. 12, no. 1, pp. 1–13, 2017.
2. J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in Proc. NAACL-HLT, 2019, pp. 4171–4186.
3. T. Brown et al., “Language models are few-shot learners,” in Advances in Neural Information Processing Systems (NeurIPS), 2020, pp. 1877–1901.
4. A. Vaswani et al., “Attention is all you need,” in Proc. NeurIPS, 2017, pp. 5998–6008.
5. Z. Yang et al., “XLNet: Generalized autoregressive pretraining for language understanding,” in Advances in Neural Information Processing Systems (NeurIPS), 2019, pp. 5754–5764.
6. R. S. Baker and K. Yacef, “The state of educational data mining in 2009: A review and future visions,” J. Educational Data Mining, vol. 1, no. 1, pp. 3–17, 2009.
7. A. M. Turing, “Computing machinery and intelligence,” Mind, vol. 59, no. 236, pp. 433–460, 1950.
8. H. Heffernan and C. Heffernan, “The ASSISTments ecosystem: Building a platform that brings scientists and teachers together for minimizing student learning gaps,” Int. J. Artificial Intelligence in Education, vol. 24, no. 4, pp. 470–497, 2014.
9. J. K. Burstein, M. Chodorow, and C. Leacock, “Automated essay evaluation: The Criterion online writing service,” AI Magazine, vol. 25, no. 3, pp. 27–36, 2004.
10. P. Ekman and W. V. Friesen, “Constants across cultures in the face and emotion,” Journal of Personality and Social Psychology, vol. 17, no. 2, pp. 124–129, 1971.
11. S. Li and W. Deng, “Deep facial expression recognition: A survey,” IEEE Trans. Affective Computing, vol. 13, no. 3, pp. 1195–1215, Jul.–Sep. 2022.
12. C. D’Mello and A. Graesser, “Multimodal semi-automated affect detection from conversational cues, gross body language, and facial features,” User Modeling and User-Adapted Interaction, vol. 20, no. 2, pp. 147–187, 2010.
13. M. Chen et al., “Emotion recognition from physiological signals using hybrid deep learning models,” IEEE Access, vol. 9, pp. 99605–99617, 2021.
14. R. E. Mayer, Multimedia Learning, 3rd ed. Cambridge, U.K.: Cambridge Univ. Press, 2021.
15. A. Hussain, M. A. A. Murad, K. H. Naeem, and F. F. Muhammad, “Artificial intelligence techniques in personalized learning systems: A review,” Education and Information Technologies, vol. 26, pp. 1721–1741, 2021.
16. Y. Bengio, A. Courville, and P. Vincent, “Representation learning: A review and new perspectives,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 35, no. 8, pp. 1798–1828, Aug. 2013.
17. M. Alqahtani and A. Kavakli, “AI in e-learning: A review of the role of chatbots,” Applied Sciences, vol. 10, no. 15, pp. 5115–5128, 2020.
18. D. Litman and S. Singh, “Intelligent tutoring systems: An overview,” AI Magazine, vol. 40, no. 4, pp. 8–15, 2019.
19. A. S. Rani, T. Srivastava, and A. Sharma, “Affective computing in education: Current trends and future directions,” IEEE Trans. Learning Technologies, vol. 14, no. 6, pp. 823–835, Dec. 2021.
20. C. Popenici and S. Kerr, “Exploring the impact of artificial intelligence on teaching and learning in higher education,” Research and Practice in Technology Enhanced Learning, vol. 12, no. 1, pp. 1–13, 2017.
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